This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction.
Estatística BayesianaUniversidade Duke
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- 5 stars45,19%
- 4 stars20,48%
- 3 stars14,59%
- 2 stars9,21%
- 1 star10,49%
Principais avaliações do ESTATÍSTICA BAYESIANA
I wanted to tools for Bayesian Statistics to be as functional as the other tools available. No problem with the class. I think the material will get there for R.
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
Very good introduction to Bayesian Statistics. Very interactive with Labs in Rmarkdown. Definitely requires thinking and a good math/analytic background is helpful.
It was a good course, though I would include more coursework and exercises in R to assist with comprehending a difficult subject. Overall, good course for something that's difficult to teach.
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